An adaptive modified neural lateral-longitudinal control system for path following of autonomous vehicles

Abstract

The full-fledged development and the practical use of autonomous vehicles (AVs) would be a great technological achievement and would substantially reduce the enormous damages caused by driving accidents to life and property. Technology based companies such as Google and Audi are getting closer to realizing the dream of seeing fully AVs on the road. A vehicle’s severely nonlinear dynamics due to the forces acting between road and vehicle tires, the coupling characteristic, and the uncertainties of parameters such as wheel moment of inertia and vehicle mass have made it rather difficult to approximate a precise mathematical model of vehicle dynamics. In this paper, to overcome these challenges we propose a model-independent control method based on improved adaptive neural controllers for path tracking control of AVs. In the structure of these improved neural controllers, we employ interval type-2 fuzzy sets (IT2FS) as activation functions. Despite the interdependence of a vehicle’s longitudinal and lateral motions, many of the research works on the path tracking of AVs have only focused on lateral motion control. By using the inputs of steering angle and torque, the presented control scheme tackles the simultaneous control of lateral and longitudinal moves. Results obtained from the lateral controller based on an improved neural network (NN) have been analyzed first at a constant velocity of 20 m/s and with/without considering parametric uncertainties. Then the longitudinal controller based on the improved NN is compared with sliding mode and common NN based controllers. Finally, the results obtained by simulating the simultaneous control of lateral and longitudinal motions indicate maximum tracking errors of 0.04 m (for lateral path following) and 0.02 m/s (for longitudinal velocity) and demonstrate the desirable performance of the proposed control approach.